Bearing fault diagnosis method based on stacked autoencoder and softmax regression

Tao, Siqin; Zhang, Tao; Yang, Jun; Wang, Xueqian; Lu, Weining

doi:10.1109/chicc.2015.7260634

Cited by 89 publications

(41 citation statements)

References 12 publications

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“…The softmax function is used as the cost function to transform the class classification problem, that is, the output of the previous layer, into the probability of each state when solving the state classification problem. The softmax function is as follows [20]:…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Fault Diagnosis of Induction Motor Using Convolutional Neural Network

2019

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Induction motors are among the most important components of modern machinery and industrial equipment. Therefore, it is necessary to develop a fault diagnosis system that detects the operating conditions of and faults in induction motors early. This paper presents an induction motor fault diagnosis system based on a CNN (convolutional neural network) model. In the proposed method, vibration signal data are obtained from the induction motor experimental environment, and these values are input into the CNN. Then, the CNN performs fault diagnosis. In this study, fault diagnosis of an induction motor is performed in three states, namely, normal, rotor fault, and bearing fault. In addition, a GUI (graphical user interface) for the proposed fault diagnosis system is presented. The experimental results confirm that the proposed method is suitable for diagnosing rotor and bearing faults of induction motors.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

Fault Diagnosis of Induction Motor Using Convolutional Neural Network

2019

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“…By reviewing the literatures on diagnosis of Alzheimer's disease, it was concluded that brain data and images with a low sample size and high dimension are one of the most important challenges in this study, and new research can be carried out in this area [12][13][14][15]. Most of the recently used methods are deep learning methods, including deep sparse multi-task learning [16], stacked auto-encoder [17], sparse regression models [18], etc., each attempting to overcome the aforementioned challenges. These methods are used more to select features.…”

Section: Related Workmentioning

confidence: 99%

“…In the features of this issue, there are features with information load and without information load that should be selected, which can improve the classification accuracy of the disease. In [17], a deep architecture for the removal of the features without information load has been recursively proposed by implementing sparse multi-task learning in a hierarchy. The optimal regression coefficients were assumed to reflect the relative importance of the features in representing the target response variables.…”

Section: Related Workmentioning

confidence: 99%

An intelligent Alzheimer’s disease diagnosis method using unsupervised feature learning

2019

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In computer systems, especially with the advancement of the Internet and databases, big data is increasingly expanding and is advancing exponentially [1-4]. This is mostly true in medical big data and images. Therefore, the issue of exploding data shows the concept and power of the big data. In the field of medicine, especially magnetic resonance imaging (MRI) images, the issue of big data with high data dimensions is investigated [5]. As people grow older in the community, an untreated disease would be common, which is called Alzheimer's, and it has been proven that it has no treatment, but it can be prevented from development with timely diagnosis. Alzheimer is known as the most common disease among the various causes of dementia and with each passing decade, the number of people infected with the disease is almost doubled. For this reason, timely

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“…That is, for each sample in training input data set, what are the real classes of these samples should be known before the training. The trained SM estimates the appropriate classes for input samples with a process based on probabilistic computations [37]. SM structures are often encountered in deep models because they can be easily integrated into AEs [38].…”

Section: Softmax Classifiermentioning

confidence: 99%

Parmak İzi̇ Tanima İçi̇n Farkli Siniflandiricilarin Karşilaştirmali Başarim Anali̇zi̇

Baştürk¹,

Baştürk²,

Qurbanov³

2018

Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi

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In this study, recognition of fingerprint images has been performed by recent classifiers as well as some important and common classifiers available in the literature. The classification methods used in the study are support vector machines, k-nearest neighbors, Naive-Bayes, decision tree learning, and deep neural networks. Training/testing data set has been obtained basically by using four different versions of fingerprint images of 165 different fingers. Additional seven rotated versions of each different fingerprint images are also used to extend the data set. Feature vector of each fingerprint image (a fingercode) has been produced by using directional Gabor filters and averaging specific regions (sectors) of their output images. After creating fingercode data set, all classifiers has been trained to recognize fingerprint images. Detailed simulation results show that deep neural networks can be effectively used among all classifiers for recognition of fingerprint images.

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Bearing fault diagnosis method based on stacked autoencoder and softmax regression

Cited by 89 publications

References 12 publications

Fault Diagnosis of Induction Motor Using Convolutional Neural Network

Fault Diagnosis of Induction Motor Using Convolutional Neural Network

An intelligent Alzheimer’s disease diagnosis method using unsupervised feature learning

Parmak İzi̇ Tanima İçi̇n Farkli Siniflandiricilarin Karşilaştirmali Başarim Anali̇zi̇

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